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Titre :
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Multilevel Regression and Poststratification for Small-Area Estimation of Population Health Outcomes : A Case Study of Chronic Obstructive Pulmonary Disease Prevalence Using the Behavioral Risk Factor Surveillance System (2014)
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Auteurs :
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. XINGYOU ZHANG ;
James-B HOLT ;
Division of Population Health, Centers for Disease Control and Prevention (Atlanta GA, Etats-Unis) ;
. HUA LU ;
Anne-G WHEATON ;
Earl-S FORD ;
Kurt-J GREENLUND ;
Janet-B CROFT
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Type de document :
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Article
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Dans :
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American journal of epidemiology (vol. 179, n° 8, avril 2014)
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Pagination :
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1025-1033
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Langues:
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Anglais
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Mots-clés :
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Régression
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Estimation
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Population
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Evolution
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Pronostic
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Etude cas
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Prévalence
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Comportement
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Facteur risque
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Surveillance
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Epidémiologie
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Résumé :
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[BDSP. Notice produite par INIST-CNRS 8R0xm7FJ. Diffusion soumise à autorisation]. A variety of small-area statistical models have been developed for health surveys, but none are sufficiently flexible to generate small-area estimates (SAEs) to meet data needs at different geographic levels. We developed a multilevel logistic model with both state-and nested county-level random effects for chronic obstructive pulmonary disease (COPD) using 2011 data from the Behavioral Risk Factor Surveillance System. We applied poststratification with the (decennial) US Census 2010 counts of census-block population to generate census-block-level SAEs of COPD prevalence which could be conveniently aggregated to all other census geographic units, such as census tracts, counties, and congressional districts. The model-based SAEs and direct survey estimates of COPD prevalence were quite consistent at both the county and state levels. The Pearson correlation coefficient was 0.99 at the state level and ranged from 0.88 to 0.95 at the county level. Our extended multilevel regression modeling and poststratification approach could be adapted for other geocoded national health surveys to generate reliable SAEs for population health outcomes at all administrative and legislative geographic levels of interest in a scalable framework.
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